Is MCP just bash with extra steps?
Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.
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Introducing APEX: Adversarial Pattern Extraction and Correlation
In this Black Hat talk, Nicole Beckwith introduces APEX (Adversarial Pattern Extraction and Correlation), a detection framework—not a Cribl product—that clusters TTP-based signals around entities to support behavioral detection. It is intended for security practitioners, SOC and detection teams, and threat hunters who want to learn how to use raw telemetry or OCSF data, TTP chaining, time windows, criticality, and cross-correlation to detect behavior beyond static indicators and rule-count coverage.
Why We Stopped Reading Agent Logs Like a Waterfall
Giordano “Gyo” Piazza, GitKraken’s Director of Engineering, has spent the past month heads down on the next version of Kepler. When he got on a call to talk through it, he skipped the changelog and went straight to the part he actually wanted to talk about: how agents show their work.
Loop Engineering Guardrails for iGaming with Claude Code and CX CLI
Tuning guardrails can take time. To accelerate this, we combined the power of Claude Code, Coralogix Telemetry and the CX CLI, to build a loop that iterates on guardrails. To test our loop, we built a fictional chat interface for an iGaming operator, that took bets, gave odds on markets and more, but with a catch: this agent was not permitted to make predictions about where the market was going. With our goal in place, we got to work designing our loop.
Shipped: Allocate AI spend by the tags on your IAM roles
The tags you put on the IAM users and roles that call your models now come through as dimensions you can group and filter on. Tag a role with costCenter, department, email, or name, group by that key, and the model spend those callers drove shows up underneath. That gives you the human-readable half of caller identity. A caller ARN like assumed-role/inference-gateway/session-8842 names a role.
Database monitoring: what to track, which tools to use, and what it all costs
Database monitoring is the continuous tracking of a database's health, performance, and resource consumption, covering metrics like query latency, throughput, connections, and replication lag. In the AI era, it has expanded to include a fifth dimension: spend. Modern teams monitor not just whether the database is fast, but whether the workload it serves is worth what it costs to run. In May 2025, an engineer named Bohan Zhang stood up at PGConf.dev in Montreal and broke every scaling rule in the book.
AI coding tools cost compared: Copilot, Cursor, Claude Code & Codex
AI coding tools cost between $0, on GitHub Copilot's free tier, and $200 per developer per month at list price. GitHub Copilot is cheapest to start at $10. Codex is cheapest per task for ChatGPT subscribers. Claude Code has the most predictable ceiling. Cursor is the most variable. Once agents run all day, vendor-reported usage lands at $100 to $250 per developer monthly, no matter which one you pick.
Codex vs. Claude Code in 2026: cost, capability, and which one your team should pick
Codex is OpenAI's terminal-based autonomous coding agent, bundled into ChatGPT plans and billed through token-based credits. Claude Code is Anthropic's terminal-based coding agent, sold through Claude subscriptions or the API. Codex is cheaper to enter; Claude Code is stronger on complex work. Both run $100 to $250 per developer monthly at active use.
From Code to Runtime: Hands-On Zephyr RTOS Debugging at Microchip MASTERs 2026
Johan Kraft introduces Percepio Tracealyzer during the hands-on Zephyr RTOS debugging class at Microchip MASTERs 2026 in Phoenix. Microchip MASTERs 2026 brought embedded developers together in Phoenix for several days of technical training, hands-on learning and conversations around the challenges engineers are solving every day. Percepio was there, with Founder and CTO Johan Kraft and Product Lead Andreas Klintemyr joining the Microchip community to meet developers, customers and industry partners.
The big question at Black Hat USA 2026: "how do I know?"
Black Hat USA 2026 brought more than 20,000 people to Mandalay Bay in Las Vegas. We were there as a Platinum sponsor at booth 4208, and across two days on the business hall floor we had more than 750 conversations with security engineers and architects. Almost every one of them, whatever it started as, turned into a version of the same question: how do I know? How do I know whether a vendor's AI does what the banner says? How do I know what my agents are doing on the network?